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Differential privacy is a mathematical framework for determining a quantifiable and adjustable level of privacy protection. The purpose of differential privacy is to reduce the ethical, reputational, and financial risks of sharing or using data that contains sensitive or personally identifiable information (PII) for statistical analysis, data analytics, and machine learning (ML).
Essentially, differential privacy quantifies how difficult it would be for someone to trace an aggregated data instance back to a specific individual.
The framework balances the need for data utility with the need for data privacy and ensures that useful information can be extracted from large datasets without compromising anyone’s privacy.
While traditional methods for anonymizing data can still offer a layer of protection and act as a deterrent for low-level cyberattacks, they are not robust enough to mitigate the risks associated with linkage attacks that use auxiliary information to re-identify individuals.
Differential privacy mitigates risk by ensuring that statistical and algorithmic outputs are not influenced by an individual person’s data in a dataset.
Typically, this involves adding a controlled amount of random noise to the data or the analysis results. In this context, noise is a deliberate change in data or query results that masks the presence or absence of a specific individual’s data in a dataset.
The differential privacy framework provides data owners and data holders with a structured way to assess and control acceptable risk while ensuring that aggregated data retains its usefulness for analytics and machine learning decisions.
The framework’s mathematical approach has four important advantages over previous privacy techniques:
Differential privacy makes it statistically improbable for an observer to determine whether any specific individual’s data was included in a computation. It ensures that the presence or absence of a single data point won’t significantly affect the outcome of statistical analysis, data analytics, or queries.
The most basic technique involves adding controlled amounts of random noise to either the data or query results. The noise can be added in various ways, depending on the specific differential privacy algorithm that’s been chosen.
The Laplace mechanism is one of the most popular algorithms used to implement differential privacy and add random noise. The level of noise in this mechanism is determined by two things: the privacy parameter that’s selected, and the sensitivity of the query or data operation that’s being performed.
The privacy parameter, which is typically represented by the Greek letter epsilon (ε), quantifies the acceptable level of privacy loss for each query or mathematical operation. This parameter influences the amount of noise that needs to be added to ensure privacy, and each query’s consumption of ε contributes to the total privacy loss budget for the dataset.
The privacy loss budget is the total allowable limit of privacy loss over multiple queries. Each query consumes some of this budget based on its ε value.
The choice of ε is determined by the data holder and involves a trade-off between privacy and data utility. Too much noise can reduce the data’s usefulness, while too little noise can expose the data owner or holder to financial and reputational risk.
Sensitivity measures the maximum amount a query result would change if a single record in the dataset was either included or excluded.
The change is calculated by determining the largest difference in output for all possible pairs of adjacent datasets.
In cases of high sensitivity, where a single record can significantly alter the outcome, a greater amount of noise is necessary to reduce the influence of any individual record and maintain privacy.
Differential privacy can be implemented locally or globally. Local differential privacy (LDP) requires the data owner to add noise to each data instance before sharing their data. This approach ensures privacy at the point of collection.
In contrast, global differential privacy (GDP) adds noise to the outputs of queries on the data. This approach, which may also be referred to as central differential privacy, leaves the original data untouched.
The choice between LDP and GDP often depends on the specific privacy requirements, the level of trust in the entity that is handling the data, and the need for data accuracy.
Differential privacy allows machine learning algorithms to identify patterns and learn from data without compromising the specific details of individual data points.
In theory, this means that when a differentially private machine learning model is trained, it becomes difficult (if not impossible) for attackers to reverse engineer the model and try to locate personal information in training data.
This is important because an increasing number of data privacy laws and regulations require organizations to ensure that personal data is not misused or disclosed without consent. Differential privacy helps organizations use sensitive data for analytical and predictive purposes, and still stay in compliance with regulatory mandates.
Large tech companies like Apple, Google, and Microsoft are using differential privacy to protect end-user data when they collect information for product improvement and personalized services.
Governmental agencies are also using differential privacy to protect people’s privacy when they publish statistical data. For example, the U.S. Census Bureau has started to use differential privacy to protect sensitive information in census data.
Other examples of differential privacy use cases today include:
Differential privacy is a framework that provides a quantifiable and adjustable level of privacy protection for individual data instances that are used for statistical analysis, data analytics, and training machine learning models.
The Laplace mechanism is arguably the most widely used and well-understood differential privacy algorithm.
Differential privacy is used by a wide range of entities across various market sectors, including technology companies, government agencies, research institutions, healthcare organizations, financial institutions, social media platforms, and market research firms.
Differential privacy on an iPhone refers to Apple’s implementation of local differential privacy. The data Apple collects from iPhone users is opt-in and the iOS provides privacy settings that allow users to choose what data they want to share.
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Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.
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